

def    _A							(						__snake_case  :int = 400_0000  )   -> int:



   """simple docstring"""


   __SCREAMING_SNAKE_CASE       =				[]
   __SCREAMING_SNAKE_CASE       ,   __SCREAMING_SNAKE_CASE       =				0, 1
   while b <= n:
      if b % 2 == 0:
         even_fibs.append(_lowercase  )
      __SCREAMING_SNAKE_CASE       ,   __SCREAMING_SNAKE_CASE       =				b, a + b
   return sum(_lowercase  )


if __name__ == "__main__":
     print(F"""{solution() = }""")





"""simple docstring"""



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_							=							16
lowercase_							=							32
def      UpperCAmelCase     (       _lowercase :   Accelerator			,   _lowercase :   int = 1_6			,   _lowercase :   str = "bert-base-cased"	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			AutoTokenizer.from_pretrained(_lowercase	)
	lowerCAmelCase_      							=			load_dataset('''glue'''			,   '''mrpc'''	)

	def tokenize_function(_lowercase :   Optional[int]	):
		# max_length=None => use the model max length (it's actually the default)
		lowerCAmelCase_      							=			tokenizer(examples['''sentence1''']			,   examples['''sentence2''']			,   truncation=_lowercase			,   max_length=_lowercase	)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCAmelCase_      							=			datasets.map(
	    _lowercase			,   batched=_lowercase			,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,   load_from_cache_file=_lowercase	)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCAmelCase_      							=			tokenized_datasets.rename_column('''label'''			,   '''labels'''	)

	def collate_fn(_lowercase :   Any	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowercase			,   padding='''max_length'''			,   max_length=1_2_8			,   return_tensors='''pt'''	)
		return tokenizer.pad(_lowercase			,   padding='''longest'''			,   return_tensors='''pt'''	)

	# Instantiate dataloaders.
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''train''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)
	lowerCAmelCase_      							=			DataLoader(
	    tokenized_datasets['''validation''']			,   shuffle=_lowercase			,   collate_fn=_lowercase			,   batch_size=_lowercase	)

	return train_dataloader, eval_dataloader
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   Any			,   _lowercase :   Any			,   _lowercase :   str	)						-> List[Any]:


	"""simple docstring"""




	model.eval()
	lowerCAmelCase_      							=			0
	for step, batch in enumerate(_lowercase	):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device	)
		with torch.no_grad():
			lowerCAmelCase_      							=			model(**_lowercase	)
		lowerCAmelCase_      							=			outputs.logits.argmax(dim=-1	)
		# It is slightly faster to call this once, than multiple times
		lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.gather(
		    (predictions, batch['''labels'''])	)  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(_lowercase	) - 1:
				lowerCAmelCase_      							=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
				lowerCAmelCase_      							=			references[: len(eval_dataloader.dataset	) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=_lowercase			,   references=_lowercase			,   )

	lowerCAmelCase_      							=			metric.compute()
	return eval_metric["accuracy"]
def      UpperCAmelCase     (       _lowercase :   Dict			,   _lowercase :   Optional[Any]	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCAmelCase_      							=			config['''lr''']
	lowerCAmelCase_      							=			int(config['''num_epochs''']	)
	lowerCAmelCase_      							=			int(config['''seed''']	)
	lowerCAmelCase_      							=			int(config['''batch_size''']	)
	lowerCAmelCase_      							=			args.model_name_or_path

	set_seed(_lowercase	)
	lowerCAmelCase_							,	lowerCAmelCase_      							=			get_dataloaders(_lowercase			,   _lowercase			,   _lowercase	)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCAmelCase_      							=			AutoModelForSequenceClassification.from_pretrained(_lowercase			,   return_dict=_lowercase	)

	# Instantiate optimizer
	lowerCAmelCase_      							=			(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCAmelCase_      							=			optimizer_cls(params=model.parameters()			,   lr=_lowercase	)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCAmelCase_      							=			accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		lowerCAmelCase_      							=			1
	lowerCAmelCase_      							=			(len(_lowercase	) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		lowerCAmelCase_      							=			get_linear_schedule_with_warmup(
		    optimizer=_lowercase			,   num_warmup_steps=0			,   num_training_steps=_lowercase			,   )
	else:
		lowerCAmelCase_      							=			DummyScheduler(_lowercase			,   total_num_steps=_lowercase			,   warmup_num_steps=0	)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_      							=			accelerator.prepare(
	    _lowercase			,   _lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)

	# We need to keep track of how many total steps we have iterated over
	lowerCAmelCase_      							=			0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCAmelCase_      							=			0
	lowerCAmelCase_      							=			evaluate.load('''glue'''			,   '''mrpc'''	)
	lowerCAmelCase_      							=			num_epochs

	if args.partial_train_epoch is not None:
		lowerCAmelCase_      							=			args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint	)
		lowerCAmelCase_      							=			args.resume_from_checkpoint.split('''epoch_'''	)[1]
		lowerCAmelCase_      							=			''''''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		lowerCAmelCase_      							=			int(_lowercase	) + 1
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		accelerator.print('''resumed checkpoint performance:'''			,   _lowercase	)
		accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			,   lr_scheduler.get_lr()[0]	)
		accelerator.print('''resumed optimizers\'s lr:'''			,   optimizer.param_groups[0]['''lr''']	)
		with open(os.path.join(args.output_dir			,   F"""state_{starting_epoch-1}.json"""	)			,   '''r'''	) as f:
			lowerCAmelCase_      							=			json.load(_lowercase	)
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	lowerCAmelCase_      							=			{}
	for epoch in range(_lowercase			,   _lowercase	):
		model.train()
		for step, batch in enumerate(_lowercase	):
			lowerCAmelCase_      							=			model(**_lowercase	)
			lowerCAmelCase_      							=			outputs.loss
			lowerCAmelCase_      							=			loss / gradient_accumulation_steps
			accelerator.backward(_lowercase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		lowerCAmelCase_      							=			F"""epoch_{epoch}"""
		lowerCAmelCase_      							=			os.path.join(args.output_dir			,   _lowercase	)
		accelerator.save_state(_lowercase	)
		lowerCAmelCase_      							=			evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
		lowerCAmelCase_      							=			accuracy
		lowerCAmelCase_      							=			lr_scheduler.get_lr()[0]
		lowerCAmelCase_      							=			optimizer.param_groups[0]['''lr''']
		lowerCAmelCase_      							=			epoch
		lowerCAmelCase_      							=			overall_step
		accelerator.print(F"""epoch {epoch}:"""			,   _lowercase	)

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir			,   F"""state_{epoch}.json"""	)			,   '''w'''	) as f:
				json.dump(_lowercase			,   _lowercase	)
def      UpperCAmelCase     (       )						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
	parser.add_argument(
	    '''--model_name_or_path'''			,   type=_lowercase			,   default='''bert-base-cased'''			,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,   required=_lowercase			,   )
	parser.add_argument(
	    '''--output_dir'''			,   type=_lowercase			,   default='''.'''			,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,   )
	parser.add_argument(
	    '''--resume_from_checkpoint'''			,   type=_lowercase			,   default=_lowercase			,   help='''If the training should continue from a checkpoint folder.'''			,   )
	parser.add_argument(
	    '''--partial_train_epoch'''			,   type=_lowercase			,   default=_lowercase			,   help='''If passed, the training will stop after this number of epochs.'''			,   )
	parser.add_argument(
	    '''--num_epochs'''			,   type=_lowercase			,   default=2			,   help='''Number of train epochs.'''			,   )
	lowerCAmelCase_      							=			parser.parse_args()
	lowerCAmelCase_      							=			{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

	training_function(_lowercase			,   _lowercase	)


if __name__ == "__main__":
		main()